Overview

Brought to you by YData

Dataset statistics

Number of variables34
Number of observations195196
Missing cells1394442
Missing cells (%)21.0%
Duplicate rows1259
Duplicate rows (%)0.6%
Total size in memory50.6 MiB
Average record size in memory272.0 B

Variable types

Numeric11
Categorical23

Alerts

Dataset has 1259 (0.6%) duplicate rowsDuplicates
HEARAID is highly overall correlated with HEARINGHigh correlation
HEARING is highly overall correlated with HEARAIDHigh correlation
HEIGHT is highly overall correlated with SEX and 1 other fieldsHigh correlation
NACCAGE is highly overall correlated with NACCAGEBHigh correlation
NACCAGEB is highly overall correlated with NACCAGEHigh correlation
NACCBMI is highly overall correlated with WEIGHTHigh correlation
NACCFAM is highly overall correlated with NACCMOMHigh correlation
NACCMOM is highly overall correlated with NACCFAMHigh correlation
SEX is highly overall correlated with HEIGHTHigh correlation
SMOKYRS is highly overall correlated with TOBAC100High correlation
TOBAC100 is highly overall correlated with SMOKYRSHigh correlation
WEIGHT is highly overall correlated with HEIGHT and 1 other fieldsHigh correlation
HISPANIC is highly imbalanced (63.3%)Imbalance
HANDED is highly imbalanced (65.3%)Imbalance
ALCOHOL is highly imbalanced (77.9%)Imbalance
CVHATT is highly imbalanced (76.6%)Imbalance
CVAFIB is highly imbalanced (68.7%)Imbalance
CVCHF is highly imbalanced (85.7%)Imbalance
CBSTROKE is highly imbalanced (77.6%)Imbalance
CBTIA is highly imbalanced (76.4%)Imbalance
DIABETES is highly imbalanced (59.7%)Imbalance
NACCAGE has 3596 (1.8%) missing valuesMissing
EDUC has 3869 (2.0%) missing valuesMissing
NACCFAM has 17766 (9.1%) missing valuesMissing
NACCMOM has 6192 (3.2%) missing valuesMissing
NACCDAD has 8259 (4.2%) missing valuesMissing
TOBAC100 has 72924 (37.4%) missing valuesMissing
SMOKYRS has 76680 (39.3%) missing valuesMissing
ALCOHOL has 72076 (36.9%) missing valuesMissing
CVHATT has 72060 (36.9%) missing valuesMissing
CVAFIB has 72261 (37.0%) missing valuesMissing
CVCHF has 72017 (36.9%) missing valuesMissing
CBSTROKE has 72131 (37.0%) missing valuesMissing
CBTIA has 72822 (37.3%) missing valuesMissing
DIABETES has 72087 (36.9%) missing valuesMissing
HYPERTEN has 72089 (36.9%) missing valuesMissing
HYPERCHO has 72800 (37.3%) missing valuesMissing
NACCTBI has 72904 (37.3%) missing valuesMissing
APNEA has 176330 (90.3%) missing valuesMissing
DEP2YRS has 72802 (37.3%) missing valuesMissing
NACCBMI has 25911 (13.3%) missing valuesMissing
HEIGHT has 26396 (13.5%) missing valuesMissing
WEIGHT has 36247 (18.6%) missing valuesMissing
HEARING has 28725 (14.7%) missing valuesMissing
HEARAID has 28061 (14.4%) missing valuesMissing
BPSYS has 39586 (20.3%) missing valuesMissing
BPDIAS has 42454 (21.7%) missing valuesMissing
SMOKYRS has 67139 (34.4%) zerosZeros

Reproduction

Analysis started2025-11-16 06:49:46.447889
Analysis finished2025-11-16 06:50:27.292197
Duration40.84 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

NACCAGE
Real number (ℝ)

High correlation  Missing 

Distinct91
Distinct (%)< 0.1%
Missing3596
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean74.101628
Minimum18
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-16T12:20:27.417065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile57
Q168
median75
Q381
95-th percentile90
Maximum110
Range92
Interquartile range (IQR)13

Descriptive statistics

Standard deviation10.20492
Coefficient of variation (CV)0.13771519
Kurtosis1.3056095
Mean74.101628
Median Absolute Deviation (MAD)7
Skewness-0.56514514
Sum14197872
Variance104.14039
MonotonicityNot monotonic
2025-11-16T12:20:27.595627image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
757882
 
4.0%
747788
 
4.0%
777778
 
4.0%
767748
 
4.0%
737616
 
3.9%
787511
 
3.8%
797423
 
3.8%
727342
 
3.8%
807132
 
3.7%
717043
 
3.6%
Other values (81)116337
59.6%
ValueCountFrequency (%)
1810
 
< 0.1%
199
 
< 0.1%
2012
 
< 0.1%
2123
< 0.1%
2224
< 0.1%
2332
< 0.1%
2425
< 0.1%
2533
< 0.1%
2638
< 0.1%
2731
< 0.1%
ValueCountFrequency (%)
1102
 
< 0.1%
1092
 
< 0.1%
1083
 
< 0.1%
1074
 
< 0.1%
1067
 
< 0.1%
10513
 
< 0.1%
10425
 
< 0.1%
10340
 
< 0.1%
10251
< 0.1%
101102
0.1%

NACCAGEB
Real number (ℝ)

High correlation 

Distinct88
Distinct (%)< 0.1%
Missing1636
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean70.930921
Minimum18
Maximum110
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-16T12:20:27.740201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile55
Q165
median71
Q378
95-th percentile85
Maximum110
Range92
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.7870752
Coefficient of variation (CV)0.13798038
Kurtosis1.4540608
Mean70.930921
Median Absolute Deviation (MAD)6
Skewness-0.58276968
Sum13729389
Variance95.786842
MonotonicityNot monotonic
2025-11-16T12:20:27.881942image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
728362
 
4.3%
738303
 
4.3%
708230
 
4.2%
757828
 
4.0%
697729
 
4.0%
747602
 
3.9%
687600
 
3.9%
677588
 
3.9%
717525
 
3.9%
777264
 
3.7%
Other values (78)115529
59.2%
ValueCountFrequency (%)
1831
< 0.1%
1919
 
< 0.1%
2013
 
< 0.1%
2135
< 0.1%
2262
< 0.1%
2322
 
< 0.1%
2424
 
< 0.1%
2537
< 0.1%
2629
< 0.1%
2744
< 0.1%
ValueCountFrequency (%)
1101
 
< 0.1%
1092
 
< 0.1%
1061
 
< 0.1%
1049
 
< 0.1%
1037
 
< 0.1%
10210
 
< 0.1%
10125
 
< 0.1%
10037
 
< 0.1%
9834
 
< 0.1%
97122
0.1%

SEX
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.3 MiB
2
113408 
1
81788 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters195196
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2113408
58.1%
181788
41.9%

Length

2025-11-16T12:20:27.999652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T12:20:28.111782image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2113408
58.1%
181788
41.9%

Most occurring characters

ValueCountFrequency (%)
2113408
58.1%
181788
41.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)195196
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2113408
58.1%
181788
41.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)195196
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2113408
58.1%
181788
41.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)195196
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2113408
58.1%
181788
41.9%

EDUC
Real number (ℝ)

Missing 

Distinct30
Distinct (%)< 0.1%
Missing3869
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean15.667334
Minimum0
Maximum31
Zeros214
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-16T12:20:28.220498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q114
median16
Q318
95-th percentile20
Maximum31
Range31
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1220872
Coefficient of variation (CV)0.19927367
Kurtosis2.2708616
Mean15.667334
Median Absolute Deviation (MAD)2
Skewness-0.85581899
Sum2997584
Variance9.7474283
MonotonicityNot monotonic
2025-11-16T12:20:28.368090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1646641
23.9%
1841211
21.1%
1229944
15.3%
1419639
10.1%
2018926
9.7%
138956
 
4.6%
156099
 
3.1%
175269
 
2.7%
195018
 
2.6%
101890
 
1.0%
Other values (20)7734
 
4.0%
(Missing)3869
 
2.0%
ValueCountFrequency (%)
0214
 
0.1%
1147
 
0.1%
2337
 
0.2%
3563
 
0.3%
4376
 
0.2%
5402
 
0.2%
61109
0.6%
7647
 
0.3%
101890
1.0%
111799
0.9%
ValueCountFrequency (%)
312
 
< 0.1%
3011
 
< 0.1%
2916
 
< 0.1%
2812
 
< 0.1%
2714
 
< 0.1%
2672
 
< 0.1%
25199
0.1%
24190
 
0.1%
23219
0.1%
22493
0.3%

MARISTAT
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing896
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1.7444364
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-16T12:20:28.480764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile5
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1966998
Coefficient of variation (CV)0.6860094
Kurtosis2.8334332
Mean1.7444364
Median Absolute Deviation (MAD)0
Skewness1.838749
Sum338944
Variance1.4320904
MonotonicityNot monotonic
2025-11-16T12:20:28.595097image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1118996
61.0%
237543
 
19.2%
322711
 
11.6%
510137
 
5.2%
63196
 
1.6%
41717
 
0.9%
(Missing)896
 
0.5%
ValueCountFrequency (%)
1118996
61.0%
237543
 
19.2%
322711
 
11.6%
41717
 
0.9%
510137
 
5.2%
63196
 
1.6%
ValueCountFrequency (%)
63196
 
1.6%
510137
 
5.2%
41717
 
0.9%
322711
 
11.6%
237543
 
19.2%
1118996
61.0%

NACCLIVS
Categorical

Distinct5
Distinct (%)< 0.1%
Missing324
Missing (%)0.2%
Memory size9.7 MiB
2.0
114901 
1.0
49827 
3.0
15487 
4.0
 
10916
5.0
 
3741

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters584616
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row2.0
3rd row2.0
4th row2.0
5th row1.0

Common Values

ValueCountFrequency (%)
2.0114901
58.9%
1.049827
25.5%
3.015487
 
7.9%
4.010916
 
5.6%
5.03741
 
1.9%
(Missing)324
 
0.2%

Length

2025-11-16T12:20:28.706797image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T12:20:28.807565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2.0114901
59.0%
1.049827
25.6%
3.015487
 
7.9%
4.010916
 
5.6%
5.03741
 
1.9%

Most occurring characters

ValueCountFrequency (%)
.194872
33.3%
0194872
33.3%
2114901
19.7%
149827
 
8.5%
315487
 
2.6%
410916
 
1.9%
53741
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)584616
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.194872
33.3%
0194872
33.3%
2114901
19.7%
149827
 
8.5%
315487
 
2.6%
410916
 
1.9%
53741
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)584616
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.194872
33.3%
0194872
33.3%
2114901
19.7%
149827
 
8.5%
315487
 
2.6%
410916
 
1.9%
53741
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)584616
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.194872
33.3%
0194872
33.3%
2114901
19.7%
149827
 
8.5%
315487
 
2.6%
410916
 
1.9%
53741
 
0.6%

RACE
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing832
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean1.8438806
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-16T12:20:28.897328image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum50
Range49
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.3765611
Coefficient of variation (CV)2.9158944
Kurtosis74.850209
Mean1.8438806
Median Absolute Deviation (MAD)0
Skewness8.6934278
Sum358384
Variance28.907409
MonotonicityNot monotonic
2025-11-16T12:20:28.993070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1159740
81.8%
225870
 
13.3%
54993
 
2.6%
502351
 
1.2%
31251
 
0.6%
4159
 
0.1%
(Missing)832
 
0.4%
ValueCountFrequency (%)
1159740
81.8%
225870
 
13.3%
31251
 
0.6%
4159
 
0.1%
54993
 
2.6%
502351
 
1.2%
ValueCountFrequency (%)
502351
 
1.2%
54993
 
2.6%
4159
 
0.1%
31251
 
0.6%
225870
 
13.3%
1159740
81.8%

HISPANIC
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing677
Missing (%)0.3%
Memory size9.7 MiB
0.0
180829 
1.0
 
13690

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters583557
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0180829
92.6%
1.013690
 
7.0%
(Missing)677
 
0.3%

Length

2025-11-16T12:20:29.096772image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T12:20:29.186529image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0180829
93.0%
1.013690
 
7.0%

Most occurring characters

ValueCountFrequency (%)
0375348
64.3%
.194519
33.3%
113690
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)583557
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0375348
64.3%
.194519
33.3%
113690
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)583557
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0375348
64.3%
.194519
33.3%
113690
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)583557
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0375348
64.3%
.194519
33.3%
113690
 
2.3%

HANDED
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing1032
Missing (%)0.5%
Memory size9.7 MiB
2.0
174371 
1.0
 
15733
3.0
 
4060

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters582492
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0174371
89.3%
1.015733
 
8.1%
3.04060
 
2.1%
(Missing)1032
 
0.5%

Length

2025-11-16T12:20:29.285228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T12:20:29.487727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
2.0174371
89.8%
1.015733
 
8.1%
3.04060
 
2.1%

Most occurring characters

ValueCountFrequency (%)
.194164
33.3%
0194164
33.3%
2174371
29.9%
115733
 
2.7%
34060
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)582492
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
.194164
33.3%
0194164
33.3%
2174371
29.9%
115733
 
2.7%
34060
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)582492
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
.194164
33.3%
0194164
33.3%
2174371
29.9%
115733
 
2.7%
34060
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)582492
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
.194164
33.3%
0194164
33.3%
2174371
29.9%
115733
 
2.7%
34060
 
0.7%

NACCFAM
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing17766
Missing (%)9.1%
Memory size9.7 MiB
1.0
109780 
0.0
67650 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters532290
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0109780
56.2%
0.067650
34.7%
(Missing)17766
 
9.1%

Length

2025-11-16T12:20:29.596395image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T12:20:29.696130image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0109780
61.9%
0.067650
38.1%

Most occurring characters

ValueCountFrequency (%)
0245080
46.0%
.177430
33.3%
1109780
20.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)532290
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0245080
46.0%
.177430
33.3%
1109780
20.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)532290
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0245080
46.0%
.177430
33.3%
1109780
20.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)532290
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0245080
46.0%
.177430
33.3%
1109780
20.6%

NACCMOM
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing6192
Missing (%)3.2%
Memory size9.7 MiB
0.0
118225 
1.0
70779 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters567012
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0118225
60.6%
1.070779
36.3%
(Missing)6192
 
3.2%

Length

2025-11-16T12:20:29.796860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T12:20:29.888615image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0118225
62.6%
1.070779
37.4%

Most occurring characters

ValueCountFrequency (%)
0307229
54.2%
.189004
33.3%
170779
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)567012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0307229
54.2%
.189004
33.3%
170779
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)567012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0307229
54.2%
.189004
33.3%
170779
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)567012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0307229
54.2%
.189004
33.3%
170779
 
12.5%

NACCDAD
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing8259
Missing (%)4.2%
Memory size9.7 MiB
0.0
151869 
1.0
35068 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters560811
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0151869
77.8%
1.035068
 
18.0%
(Missing)8259
 
4.2%

Length

2025-11-16T12:20:29.989346image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T12:20:30.079105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0151869
81.2%
1.035068
 
18.8%

Most occurring characters

ValueCountFrequency (%)
0338806
60.4%
.186937
33.3%
135068
 
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)560811
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0338806
60.4%
.186937
33.3%
135068
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)560811
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0338806
60.4%
.186937
33.3%
135068
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)560811
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0338806
60.4%
.186937
33.3%
135068
 
6.3%

TOBAC100
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing72924
Missing (%)37.4%
Memory size10.0 MiB
0.0
67157 
1.0
55115 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters366816
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.067157
34.4%
1.055115
28.2%
(Missing)72924
37.4%

Length

2025-11-16T12:20:30.185766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T12:20:30.275136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.067157
54.9%
1.055115
45.1%

Most occurring characters

ValueCountFrequency (%)
0189429
51.6%
.122272
33.3%
155115
 
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)366816
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0189429
51.6%
.122272
33.3%
155115
 
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)366816
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0189429
51.6%
.122272
33.3%
155115
 
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)366816
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0189429
51.6%
.122272
33.3%
155115
 
15.0%

SMOKYRS
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct82
Distinct (%)0.1%
Missing76680
Missing (%)39.3%
Infinite0
Infinite (%)0.0%
Mean9.8847919
Minimum0
Maximum84
Zeros67139
Zeros (%)34.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-16T12:20:30.384849image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q318
95-th percentile43
Maximum84
Range84
Interquartile range (IQR)18

Descriptive statistics

Standard deviation15.107277
Coefficient of variation (CV)1.5283354
Kurtosis1.4683876
Mean9.8847919
Median Absolute Deviation (MAD)0
Skewness1.5208037
Sum1171506
Variance228.22982
MonotonicityNot monotonic
2025-11-16T12:20:30.529459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
067139
34.4%
205680
 
2.9%
104352
 
2.2%
303757
 
1.9%
153089
 
1.6%
252574
 
1.3%
402463
 
1.3%
52260
 
1.2%
21632
 
0.8%
41402
 
0.7%
Other values (72)24168
 
12.4%
(Missing)76680
39.3%
ValueCountFrequency (%)
067139
34.4%
11340
 
0.7%
21632
 
0.8%
31330
 
0.7%
41402
 
0.7%
52260
 
1.2%
6906
 
0.5%
7730
 
0.4%
104352
 
2.2%
11379
 
0.2%
ValueCountFrequency (%)
841
 
< 0.1%
824
 
< 0.1%
813
 
< 0.1%
8015
< 0.1%
791
 
< 0.1%
783
 
< 0.1%
771
 
< 0.1%
767
< 0.1%
7513
< 0.1%
748
< 0.1%

ALCOHOL
Categorical

Imbalance  Missing 

Distinct3
Distinct (%)< 0.1%
Missing72076
Missing (%)36.9%
Memory size10.0 MiB
0.0
116054 
2.0
 
6121
1.0
 
945

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters369360
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0116054
59.5%
2.06121
 
3.1%
1.0945
 
0.5%
(Missing)72076
36.9%

Length

2025-11-16T12:20:30.650095image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T12:20:30.757807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0116054
94.3%
2.06121
 
5.0%
1.0945
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0239174
64.8%
.123120
33.3%
26121
 
1.7%
1945
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)369360
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0239174
64.8%
.123120
33.3%
26121
 
1.7%
1945
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)369360
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0239174
64.8%
.123120
33.3%
26121
 
1.7%
1945
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)369360
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0239174
64.8%
.123120
33.3%
26121
 
1.7%
1945
 
0.3%

CVHATT
Categorical

Imbalance  Missing 

Distinct3
Distinct (%)< 0.1%
Missing72060
Missing (%)36.9%
Memory size10.0 MiB
0.0
115537 
2.0
 
6528
1.0
 
1071

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters369408
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0115537
59.2%
2.06528
 
3.3%
1.01071
 
0.5%
(Missing)72060
36.9%

Length

2025-11-16T12:20:30.878517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T12:20:30.972234image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0115537
93.8%
2.06528
 
5.3%
1.01071
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0238673
64.6%
.123136
33.3%
26528
 
1.8%
11071
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)369408
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0238673
64.6%
.123136
33.3%
26528
 
1.8%
11071
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)369408
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0238673
64.6%
.123136
33.3%
26528
 
1.8%
11071
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)369408
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0238673
64.6%
.123136
33.3%
26528
 
1.8%
11071
 
0.3%

CVAFIB
Categorical

Imbalance  Missing 

Distinct3
Distinct (%)< 0.1%
Missing72261
Missing (%)37.0%
Memory size10.0 MiB
0.0
112450 
1.0
 
7355
2.0
 
3130

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters368805
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0112450
57.6%
1.07355
 
3.8%
2.03130
 
1.6%
(Missing)72261
37.0%

Length

2025-11-16T12:20:31.081941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T12:20:31.189689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0112450
91.5%
1.07355
 
6.0%
2.03130
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0235385
63.8%
.122935
33.3%
17355
 
2.0%
23130
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)368805
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0235385
63.8%
.122935
33.3%
17355
 
2.0%
23130
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)368805
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0235385
63.8%
.122935
33.3%
17355
 
2.0%
23130
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)368805
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0235385
63.8%
.122935
33.3%
17355
 
2.0%
23130
 
0.8%

CVCHF
Categorical

Imbalance  Missing 

Distinct3
Distinct (%)< 0.1%
Missing72017
Missing (%)36.9%
Memory size10.0 MiB
0.0
119400 
1.0
 
2501
2.0
 
1278

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters369537
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0119400
61.2%
1.02501
 
1.3%
2.01278
 
0.7%
(Missing)72017
36.9%

Length

2025-11-16T12:20:31.297403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T12:20:31.423403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0119400
96.9%
1.02501
 
2.0%
2.01278
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0242579
65.6%
.123179
33.3%
12501
 
0.7%
21278
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)369537
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0242579
65.6%
.123179
33.3%
12501
 
0.7%
21278
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)369537
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0242579
65.6%
.123179
33.3%
12501
 
0.7%
21278
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)369537
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0242579
65.6%
.123179
33.3%
12501
 
0.7%
21278
 
0.3%

CBSTROKE
Categorical

Imbalance  Missing 

Distinct3
Distinct (%)< 0.1%
Missing72131
Missing (%)37.0%
Memory size10.0 MiB
0.0
116112 
2.0
 
5504
1.0
 
1449

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters369195
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0116112
59.5%
2.05504
 
2.8%
1.01449
 
0.7%
(Missing)72131
37.0%

Length

2025-11-16T12:20:31.565022image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T12:20:31.671738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0116112
94.4%
2.05504
 
4.5%
1.01449
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0239177
64.8%
.123065
33.3%
25504
 
1.5%
11449
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)369195
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0239177
64.8%
.123065
33.3%
25504
 
1.5%
11449
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)369195
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0239177
64.8%
.123065
33.3%
25504
 
1.5%
11449
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)369195
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0239177
64.8%
.123065
33.3%
25504
 
1.5%
11449
 
0.4%

CBTIA
Categorical

Imbalance  Missing 

Distinct3
Distinct (%)< 0.1%
Missing72822
Missing (%)37.3%
Memory size10.0 MiB
0.0
115041 
2.0
 
5646
1.0
 
1687

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters367122
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0115041
58.9%
2.05646
 
2.9%
1.01687
 
0.9%
(Missing)72822
37.3%

Length

2025-11-16T12:20:31.781464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T12:20:31.876351image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0115041
94.0%
2.05646
 
4.6%
1.01687
 
1.4%

Most occurring characters

ValueCountFrequency (%)
0237415
64.7%
.122374
33.3%
25646
 
1.5%
11687
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)367122
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0237415
64.7%
.122374
33.3%
25646
 
1.5%
11687
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)367122
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0237415
64.7%
.122374
33.3%
25646
 
1.5%
11687
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)367122
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0237415
64.7%
.122374
33.3%
25646
 
1.5%
11687
 
0.5%

DIABETES
Categorical

Imbalance  Missing 

Distinct3
Distinct (%)< 0.1%
Missing72087
Missing (%)36.9%
Memory size10.0 MiB
0.0
106005 
1.0
15703 
2.0
 
1401

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters369327
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0106005
54.3%
1.015703
 
8.0%
2.01401
 
0.7%
(Missing)72087
36.9%

Length

2025-11-16T12:20:31.993004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T12:20:32.104738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0106005
86.1%
1.015703
 
12.8%
2.01401
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0229114
62.0%
.123109
33.3%
115703
 
4.3%
21401
 
0.4%

Most occurring categories

ValueCountFrequency (%)
(unknown)369327
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0229114
62.0%
.123109
33.3%
115703
 
4.3%
21401
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown)369327
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0229114
62.0%
.123109
33.3%
115703
 
4.3%
21401
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown)369327
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0229114
62.0%
.123109
33.3%
115703
 
4.3%
21401
 
0.4%

HYPERTEN
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing72089
Missing (%)36.9%
Memory size10.0 MiB
1.0
61900 
0.0
55268 
2.0
 
5939

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters369321
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.061900
31.7%
0.055268
28.3%
2.05939
 
3.0%
(Missing)72089
36.9%

Length

2025-11-16T12:20:32.243480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T12:20:32.379422image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.061900
50.3%
0.055268
44.9%
2.05939
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0178375
48.3%
.123107
33.3%
161900
 
16.8%
25939
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)369321
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0178375
48.3%
.123107
33.3%
161900
 
16.8%
25939
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)369321
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0178375
48.3%
.123107
33.3%
161900
 
16.8%
25939
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)369321
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0178375
48.3%
.123107
33.3%
161900
 
16.8%
25939
 
1.6%

HYPERCHO
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing72800
Missing (%)37.3%
Memory size10.0 MiB
1.0
61682 
0.0
53182 
2.0
7532 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters367188
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.061682
31.6%
0.053182
27.2%
2.07532
 
3.9%
(Missing)72800
37.3%

Length

2025-11-16T12:20:32.534851image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T12:20:32.662509image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.061682
50.4%
0.053182
43.5%
2.07532
 
6.2%

Most occurring characters

ValueCountFrequency (%)
0175578
47.8%
.122396
33.3%
161682
 
16.8%
27532
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)367188
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0175578
47.8%
.122396
33.3%
161682
 
16.8%
27532
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)367188
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0175578
47.8%
.122396
33.3%
161682
 
16.8%
27532
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)367188
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0175578
47.8%
.122396
33.3%
161682
 
16.8%
27532
 
2.1%

NACCTBI
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing72904
Missing (%)37.3%
Memory size10.0 MiB
0.0
106741 
1.0
15551 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters366876
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0106741
54.7%
1.015551
 
8.0%
(Missing)72904
37.3%

Length

2025-11-16T12:20:32.785267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T12:20:32.883044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0106741
87.3%
1.015551
 
12.7%

Most occurring characters

ValueCountFrequency (%)
0229033
62.4%
.122292
33.3%
115551
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)366876
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0229033
62.4%
.122292
33.3%
115551
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)366876
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0229033
62.4%
.122292
33.3%
115551
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)366876
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0229033
62.4%
.122292
33.3%
115551
 
4.2%

APNEA
Categorical

Missing 

Distinct3
Distinct (%)< 0.1%
Missing176330
Missing (%)90.3%
Memory size10.4 MiB
0.0
14884 
1.0
3687 
2.0
 
295

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters56598
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.014884
 
7.6%
1.03687
 
1.9%
2.0295
 
0.2%
(Missing)176330
90.3%

Length

2025-11-16T12:20:32.987769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T12:20:33.088495image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.014884
78.9%
1.03687
 
19.5%
2.0295
 
1.6%

Most occurring characters

ValueCountFrequency (%)
033750
59.6%
.18866
33.3%
13687
 
6.5%
2295
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)56598
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
033750
59.6%
.18866
33.3%
13687
 
6.5%
2295
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)56598
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
033750
59.6%
.18866
33.3%
13687
 
6.5%
2295
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)56598
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
033750
59.6%
.18866
33.3%
13687
 
6.5%
2295
 
0.5%

DEP2YRS
Categorical

Missing 

Distinct2
Distinct (%)< 0.1%
Missing72802
Missing (%)37.3%
Memory size10.0 MiB
0.0
85565 
1.0
36829 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters367182
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.085565
43.8%
1.036829
18.9%
(Missing)72802
37.3%

Length

2025-11-16T12:20:33.192219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T12:20:33.304878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.085565
69.9%
1.036829
30.1%

Most occurring characters

ValueCountFrequency (%)
0207959
56.6%
.122394
33.3%
136829
 
10.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)367182
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0207959
56.6%
.122394
33.3%
136829
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)367182
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0207959
56.6%
.122394
33.3%
136829
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)367182
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0207959
56.6%
.122394
33.3%
136829
 
10.0%

NACCBMI
Real number (ℝ)

High correlation  Missing 

Distinct479
Distinct (%)0.3%
Missing25911
Missing (%)13.3%
Infinite0
Infinite (%)0.0%
Mean96.994689
Minimum9.6
Maximum888.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-16T12:20:33.473429image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum9.6
5-th percentile20.2
Q123.7
median26.8
Q331.2
95-th percentile888.8
Maximum888.8
Range879.2
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation235.33193
Coefficient of variation (CV)2.4262353
Kurtosis7.405259
Mean96.994689
Median Absolute Deviation (MAD)3.5
Skewness3.0658013
Sum16419746
Variance55381.12
MonotonicityNot monotonic
2025-11-16T12:20:33.799556image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
888.813734
 
7.0%
25.81957
 
1.0%
25.11846
 
0.9%
26.61819
 
0.9%
231692
 
0.9%
25.51595
 
0.8%
24.41592
 
0.8%
241553
 
0.8%
25.21552
 
0.8%
28.31523
 
0.8%
Other values (469)140422
71.9%
(Missing)25911
 
13.3%
ValueCountFrequency (%)
9.61
< 0.1%
9.91
< 0.1%
10.31
< 0.1%
10.51
< 0.1%
11.61
< 0.1%
11.91
< 0.1%
12.11
< 0.1%
12.22
< 0.1%
12.62
< 0.1%
12.82
< 0.1%
ValueCountFrequency (%)
888.813734
7.0%
84.21
 
< 0.1%
68.61
 
< 0.1%
66.81
 
< 0.1%
64.91
 
< 0.1%
64.41
 
< 0.1%
641
 
< 0.1%
62.61
 
< 0.1%
62.41
 
< 0.1%
62.31
 
< 0.1%

HEIGHT
Real number (ℝ)

High correlation  Missing 

Distinct288
Distinct (%)0.2%
Missing26396
Missing (%)13.5%
Infinite0
Infinite (%)0.0%
Mean67.151041
Minimum37
Maximum88.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-16T12:20:34.091775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile59.7
Q163
median66
Q369.2
95-th percentile88.8
Maximum88.8
Range51.8
Interquartile range (IQR)6.2

Descriptive statistics

Standard deviation7.0085664
Coefficient of variation (CV)0.10437018
Kurtosis3.5266403
Mean67.151041
Median Absolute Deviation (MAD)3
Skewness1.8010321
Sum11335096
Variance49.120002
MonotonicityNot monotonic
2025-11-16T12:20:34.287252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88.811445
 
5.9%
649361
 
4.8%
639257
 
4.7%
658887
 
4.6%
628881
 
4.5%
668656
 
4.4%
677931
 
4.1%
687418
 
3.8%
696724
 
3.4%
706509
 
3.3%
Other values (278)83731
42.9%
(Missing)26396
 
13.5%
ValueCountFrequency (%)
371
 
< 0.1%
465
 
< 0.1%
46.51
 
< 0.1%
472
 
< 0.1%
486
 
< 0.1%
48.11
 
< 0.1%
48.31
 
< 0.1%
48.52
 
< 0.1%
48.82
 
< 0.1%
4919
< 0.1%
ValueCountFrequency (%)
88.811445
5.9%
841
 
< 0.1%
822
 
< 0.1%
81.31
 
< 0.1%
812
 
< 0.1%
80.91
 
< 0.1%
80.71
 
< 0.1%
808
 
< 0.1%
79.81
 
< 0.1%
79.53
 
< 0.1%

WEIGHT
Real number (ℝ)

High correlation  Missing 

Distinct324
Distinct (%)0.2%
Missing36247
Missing (%)18.6%
Infinite0
Infinite (%)0.0%
Mean166.30408
Minimum54
Maximum443
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-16T12:20:34.853363image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum54
5-th percentile113
Q1139
median162
Q3188
95-th percentile234
Maximum443
Range389
Interquartile range (IQR)49

Descriptive statistics

Standard deviation37.610357
Coefficient of variation (CV)0.22615414
Kurtosis1.0723658
Mean166.30408
Median Absolute Deviation (MAD)24
Skewness0.75145521
Sum26433868
Variance1414.5389
MonotonicityNot monotonic
2025-11-16T12:20:35.198442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1502767
 
1.4%
1602763
 
1.4%
1702431
 
1.2%
1402379
 
1.2%
1802334
 
1.2%
1652155
 
1.1%
1302136
 
1.1%
1552035
 
1.0%
1452016
 
1.0%
1751936
 
1.0%
Other values (314)135997
69.7%
(Missing)36247
 
18.6%
ValueCountFrequency (%)
541
 
< 0.1%
551
 
< 0.1%
571
 
< 0.1%
621
 
< 0.1%
632
< 0.1%
652
< 0.1%
662
< 0.1%
672
< 0.1%
681
 
< 0.1%
703
< 0.1%
ValueCountFrequency (%)
4431
 
< 0.1%
4241
 
< 0.1%
4201
 
< 0.1%
4091
 
< 0.1%
4051
 
< 0.1%
4041
 
< 0.1%
4006
< 0.1%
3991
 
< 0.1%
3962
 
< 0.1%
3932
 
< 0.1%

HEARING
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing28725
Missing (%)14.7%
Memory size9.8 MiB
1.0
124687 
0.0
41784 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters499413
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0124687
63.9%
0.041784
 
21.4%
(Missing)28725
 
14.7%

Length

2025-11-16T12:20:35.378956image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T12:20:35.534542image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1.0124687
74.9%
0.041784
 
25.1%

Most occurring characters

ValueCountFrequency (%)
0208255
41.7%
.166471
33.3%
1124687
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)499413
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0208255
41.7%
.166471
33.3%
1124687
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)499413
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0208255
41.7%
.166471
33.3%
1124687
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)499413
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0208255
41.7%
.166471
33.3%
1124687
25.0%

HEARAID
Categorical

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing28061
Missing (%)14.4%
Memory size9.8 MiB
0.0
138400 
1.0
28735 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters501405
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0138400
70.9%
1.028735
 
14.7%
(Missing)28061
 
14.4%

Length

2025-11-16T12:20:35.683143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T12:20:35.814833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0138400
82.8%
1.028735
 
17.2%

Most occurring characters

ValueCountFrequency (%)
0305535
60.9%
.167135
33.3%
128735
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)501405
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0305535
60.9%
.167135
33.3%
128735
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)501405
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0305535
60.9%
.167135
33.3%
128735
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)501405
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0305535
60.9%
.167135
33.3%
128735
 
5.7%

BPSYS
Real number (ℝ)

Missing 

Distinct163
Distinct (%)0.1%
Missing39586
Missing (%)20.3%
Infinite0
Infinite (%)0.0%
Mean138.08617
Minimum60
Maximum777
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-16T12:20:35.976360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile106
Q1120
median132
Q3145
95-th percentile168
Maximum777
Range717
Interquartile range (IQR)25

Descriptive statistics

Standard deviation56.439911
Coefficient of variation (CV)0.40872964
Kurtosis110.70373
Mean138.08617
Median Absolute Deviation (MAD)12
Skewness10.03217
Sum21487589
Variance3185.4636
MonotonicityNot monotonic
2025-11-16T12:20:36.203751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1309046
 
4.6%
1407667
 
3.9%
1207463
 
3.8%
1105045
 
2.6%
1384751
 
2.4%
1284709
 
2.4%
1324493
 
2.3%
1224335
 
2.2%
1504237
 
2.2%
1184186
 
2.1%
Other values (153)99678
51.1%
(Missing)39586
 
20.3%
ValueCountFrequency (%)
601
 
< 0.1%
681
 
< 0.1%
691
 
< 0.1%
707
< 0.1%
711
 
< 0.1%
724
< 0.1%
732
 
< 0.1%
742
 
< 0.1%
756
< 0.1%
764
< 0.1%
ValueCountFrequency (%)
7771077
0.6%
2321
 
< 0.1%
2308
 
< 0.1%
2293
 
< 0.1%
2271
 
< 0.1%
2261
 
< 0.1%
2254
 
< 0.1%
2243
 
< 0.1%
2234
 
< 0.1%
2222
 
< 0.1%

BPDIAS
Real number (ℝ)

Missing 

Distinct109
Distinct (%)0.1%
Missing42454
Missing (%)21.7%
Infinite0
Infinite (%)0.0%
Mean79.432985
Minimum30
Maximum777
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-11-16T12:20:36.390254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile59
Q168
median74
Q380
95-th percentile92
Maximum777
Range747
Interquartile range (IQR)12

Descriptive statistics

Standard deviation59.678535
Coefficient of variation (CV)0.75130671
Kurtosis128.63218
Mean79.432985
Median Absolute Deviation (MAD)6
Skewness11.253991
Sum12132753
Variance3561.5275
MonotonicityNot monotonic
2025-11-16T12:20:36.588753image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7015083
 
7.7%
8013600
 
7.0%
787755
 
4.0%
727082
 
3.6%
606574
 
3.4%
685869
 
3.0%
825811
 
3.0%
765641
 
2.9%
745439
 
2.8%
754485
 
2.3%
Other values (99)75403
38.6%
(Missing)42454
21.7%
ValueCountFrequency (%)
308
 
< 0.1%
311
 
< 0.1%
327
 
< 0.1%
335
 
< 0.1%
343
 
< 0.1%
353
 
< 0.1%
365
 
< 0.1%
377
 
< 0.1%
3823
< 0.1%
399
 
< 0.1%
ValueCountFrequency (%)
7771077
0.6%
1404
 
< 0.1%
1391
 
< 0.1%
1384
 
< 0.1%
1372
 
< 0.1%
1361
 
< 0.1%
1354
 
< 0.1%
1335
 
< 0.1%
1323
 
< 0.1%
1314
 
< 0.1%

NACCUDSD
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.3 MiB
1
100263 
0
94933 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters195196
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1100263
51.4%
094933
48.6%

Length

2025-11-16T12:20:36.757272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-16T12:20:36.895900image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1100263
51.4%
094933
48.6%

Most occurring characters

ValueCountFrequency (%)
1100263
51.4%
094933
48.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)195196
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1100263
51.4%
094933
48.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)195196
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1100263
51.4%
094933
48.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)195196
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1100263
51.4%
094933
48.6%

Interactions

2025-11-16T12:20:23.209547image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:11.271194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:12.555786image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:13.698293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:14.880835image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:16.197412image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:17.335087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:18.370962image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:19.460801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:20.605044image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:21.987591image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:23.316304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:11.398176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:12.666043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:13.802018image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:14.985553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:16.304159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:17.427239image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:18.462657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:19.556545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:20.845433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:22.099293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:23.418032image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:11.526505image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:12.766572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:13.902658image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:15.099252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:16.403894image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:17.516178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:18.558087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:19.652288image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:20.950121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:22.219970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:23.530381image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:11.647215image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:12.874284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:14.008378image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:15.206692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:16.507385image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:17.614914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:18.652410image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:19.751147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:21.076783image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:22.335661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:23.634981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:11.760299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:12.985026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:14.123069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:15.432056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:16.607997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:17.704070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:18.748153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:19.851588image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:21.193140image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:22.442341image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:23.734270image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:11.856010image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:13.082762image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:14.218888image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:15.549784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:16.699760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:17.794507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:18.833925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:19.937963image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:21.296202image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:22.538117image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:23.853982image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:11.958738image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:13.182504image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:14.322603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:15.655492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:16.794616image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:17.882281image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:18.929703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:20.045692image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:21.411611image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:22.638815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:23.965651image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:12.076421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:13.280636image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:14.421825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:15.754228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:16.892714image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:17.971034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:19.021461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:20.143414image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:21.522871image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:22.741643image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:24.088325image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:12.188267image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:13.381365image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:14.528910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:15.859237image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:16.998178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:18.064749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:19.137254image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:20.255976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:21.644099image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:22.848358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:24.217977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:12.311945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:13.486801image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:14.639565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:15.967029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:17.119821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:18.159496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:19.248652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:20.374660image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:21.762847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:22.966043image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:24.336125image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:12.425603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:13.588566image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:14.761334image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:16.083718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:17.231366image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:18.254274image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:19.354096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:20.487359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:21.875543image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-11-16T12:20:23.094698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-11-16T12:20:37.038174image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ALCOHOLAPNEABPDIASBPSYSCBSTROKECBTIACVAFIBCVCHFCVHATTDEP2YRSDIABETESEDUCHANDEDHEARAIDHEARINGHEIGHTHISPANICHYPERCHOHYPERTENMARISTATNACCAGENACCAGEBNACCBMINACCDADNACCFAMNACCLIVSNACCMOMNACCTBINACCUDSDRACESEXSMOKYRSTOBAC100WEIGHT
ALCOHOL1.0000.0340.0080.0080.0260.0120.0180.0170.0260.0950.0240.0400.0110.0090.0120.0630.0230.0200.0250.0430.0360.0370.0120.0090.0030.0230.0000.0760.0690.0260.1300.1280.1530.052
APNEA0.0341.0000.0120.0140.0290.0360.0600.0450.0410.1040.0900.0150.0140.0720.0690.0920.0000.0880.1160.0420.0500.0500.0000.0090.0000.0260.0000.0540.0490.0000.1810.0260.0290.210
BPDIAS0.0080.0121.0000.4810.0060.0010.0020.0000.0010.0000.0000.0100.0000.0060.0040.0560.0150.0070.019-0.017-0.168-0.1620.1270.0120.0100.0160.0140.0140.0420.0580.000-0.0250.0020.155
BPSYS0.0080.0140.4811.0000.0150.0140.0120.0130.0000.0200.032-0.0620.0080.0220.024-0.0350.0160.0350.1380.0190.1260.1270.0980.0360.0220.0230.0290.0150.0540.0710.0080.0150.0080.074
CBSTROKE0.0260.0290.0060.0151.0000.1320.0600.0720.0680.0420.0550.0580.0010.0210.0320.0340.0310.0510.0800.0440.0870.0860.0380.0200.0270.0540.0420.0130.0930.0390.0200.0500.0210.011
CBTIA0.0120.0360.0010.0140.1321.0000.0570.0540.0460.0290.0310.0170.0100.0580.0610.0150.0110.0500.0700.0420.1030.0940.0080.0340.0190.0290.0240.0230.0480.0100.0230.0310.0210.015
CVAFIB0.0180.0600.0020.0120.0600.0571.0000.1520.0840.0050.0090.0260.0120.0730.0750.0430.0480.0420.0660.0330.1220.1140.0030.0300.0300.0230.0290.0240.0200.0220.0750.0280.0340.027
CVCHF0.0170.0450.0000.0130.0720.0540.1521.0000.1400.0290.0650.0300.0120.0430.0560.0240.0100.0410.0740.0500.1060.1000.0240.0340.0290.0460.0400.0080.0280.0240.0190.0370.0230.034
CVHATT0.0260.0410.0010.0000.0680.0460.0840.1401.0000.0090.0660.0280.0060.0510.0570.0280.0120.1030.0890.0240.0800.0780.0150.0310.0240.0080.0320.0130.0370.0180.1100.0660.0620.035
DEP2YRS0.0950.1040.0000.0200.0420.0290.0050.0290.0091.0000.0260.0640.0070.0090.0000.0460.0530.0340.0370.0240.0870.0860.0360.0170.0190.0810.0180.0430.2250.0520.0340.0420.0300.016
DIABETES0.0240.0900.0000.0320.0550.0310.0090.0650.0660.0261.0000.0920.0080.0300.0160.0100.1040.1620.1670.0420.0480.0490.0130.0300.0420.0440.0460.0100.0310.0890.0360.0460.0350.122
EDUC0.0400.0150.010-0.0620.0580.0170.0260.0300.0280.0640.0921.0000.0320.0730.0550.1470.3970.0310.089-0.098-0.023-0.048-0.1040.0700.0450.1030.0730.0320.146-0.1430.202-0.0880.0730.011
HANDED0.0110.0140.0000.0080.0010.0100.0120.0120.0060.0070.0080.0321.0000.0120.0130.0310.0390.0040.0130.0320.0330.0360.0050.0180.0120.0260.0130.0150.0060.0160.0480.0170.0100.026
HEARAID0.0090.0720.0060.0220.0210.0580.0730.0430.0510.0090.0300.0730.0121.0000.7350.0600.0560.0240.0360.0860.2740.2340.0110.0110.0200.0430.0340.0150.0000.0380.1260.0440.0340.034
HEARING0.0120.0690.0040.0240.0320.0610.0750.0560.0570.0000.0160.0550.0130.7351.0000.0560.0340.0330.0480.0830.2810.2520.0070.0200.0210.0210.0400.0200.0370.0280.1370.0510.0390.032
HEIGHT0.0630.0920.056-0.0350.0340.0150.0430.0240.0280.0460.0100.1470.0310.0600.0561.0000.1690.0280.049-0.221-0.128-0.1080.2060.0610.0280.1390.0390.0930.125-0.1010.6490.0650.0930.528
HISPANIC0.0230.0000.0150.0160.0310.0110.0480.0100.0120.0530.1040.3970.0390.0560.0340.1691.0000.0280.0420.0920.0350.0250.0150.0300.0100.1420.0230.0140.0390.3710.0500.0490.0500.051
HYPERCHO0.0200.0880.0070.0350.0510.0500.0420.0410.1030.0340.1620.0310.0040.0240.0330.0280.0281.0000.2730.0250.1220.1180.0360.0220.0140.0430.0130.0130.0360.0170.0640.0580.0680.080
HYPERTEN0.0250.1160.0190.1380.0800.0700.0660.0740.0890.0370.1670.0890.0130.0360.0480.0490.0420.2731.0000.0950.1660.1610.0190.0830.0630.0840.0730.0250.0480.0410.0240.0640.0550.100
MARISTAT0.0430.042-0.0170.0190.0440.0420.0330.0500.0240.0240.042-0.0980.0320.0860.083-0.2210.0920.0250.0951.0000.1180.0990.0190.0790.0570.4530.0840.0530.1020.1840.3430.0330.040-0.124
NACCAGE0.0360.050-0.1680.1260.0870.1030.1220.1060.0800.0870.048-0.0230.0330.2740.281-0.1280.0350.1220.1660.1181.0000.921-0.1020.1330.0980.1320.1430.0580.107-0.0320.0360.0750.098-0.199
NACCAGEB0.0370.050-0.1620.1270.0860.0940.1140.1000.0780.0860.049-0.0480.0360.2340.252-0.1080.0250.1180.1610.0990.9211.000-0.0920.1550.1200.1210.1710.0610.152-0.0190.0450.0700.095-0.188
NACCBMI0.0120.0000.1270.0980.0380.0080.0030.0240.0150.0360.013-0.1040.0050.0110.0070.2060.0150.0360.0190.019-0.102-0.0921.0000.0100.0190.0980.0290.0000.1070.1030.0050.0250.0250.776
NACCDAD0.0090.0090.0120.0360.0200.0340.0300.0340.0310.0170.0300.0700.0180.0110.0200.0610.0300.0220.0830.0790.1330.1550.0101.0000.3970.0670.0020.0150.0230.0100.0330.0410.0100.039
NACCFAM0.0030.0000.0100.0220.0270.0190.0300.0290.0240.0190.0420.0450.0120.0200.0210.0280.0100.0140.0630.0570.0980.1200.0190.3971.0000.0410.6450.0030.0260.0040.0230.0340.0120.010
NACCLIVS0.0230.0260.0160.0230.0540.0290.0230.0460.0080.0810.0440.1030.0260.0430.0210.1390.1420.0430.0840.4530.1320.1210.0980.0670.0411.0000.0580.0460.2180.0970.3180.0440.0460.075
NACCMOM0.0000.0000.0140.0290.0420.0240.0290.0400.0320.0180.0460.0730.0130.0340.0400.0390.0230.0130.0730.0840.1430.1710.0290.0020.6450.0581.0000.0040.0640.0090.0400.0360.0170.014
NACCTBI0.0760.0540.0140.0150.0130.0230.0240.0080.0130.0430.0100.0320.0150.0150.0200.0930.0140.0130.0250.0530.0580.0610.0000.0150.0030.0460.0041.0000.0290.0070.1120.0280.0350.077
NACCUDSD0.0690.0490.0420.0540.0930.0480.0200.0280.0370.2250.0310.1460.0060.0000.0370.1250.0390.0360.0480.1020.1070.1520.1070.0230.0260.2180.0640.0291.0000.0490.1420.0370.0000.032
RACE0.0260.0000.0580.0710.0390.0100.0220.0240.0180.0520.089-0.1430.0160.0380.028-0.1010.3710.0170.0410.184-0.032-0.0190.1030.0100.0040.0970.0090.0070.0491.0000.021-0.0020.0160.032
SEX0.1300.1810.0000.0080.0200.0230.0750.0190.1100.0340.0360.2020.0480.1260.1370.6490.0500.0640.0240.3430.0360.0450.0050.0330.0230.3180.0400.1120.1420.0211.0000.1020.1050.434
SMOKYRS0.1280.026-0.0250.0150.0500.0310.0280.0370.0660.0420.046-0.0880.0170.0440.0510.0650.0490.0580.0640.0330.0750.0700.0250.0410.0340.0440.0360.0280.037-0.0020.1021.0000.8440.085
TOBAC1000.1530.0290.0020.0080.0210.0210.0340.0230.0620.0300.0350.0730.0100.0340.0390.0930.0500.0680.0550.0400.0980.0950.0250.0100.0120.0460.0170.0350.0000.0160.1050.8441.0000.079
WEIGHT0.0520.2100.1550.0740.0110.0150.0270.0340.0350.0160.1220.0110.0260.0340.0320.5280.0510.0800.100-0.124-0.199-0.1880.7760.0390.0100.0750.0140.0770.0320.0320.4340.0850.0791.000

Missing values

2025-11-16T12:20:24.561859image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-16T12:20:25.221096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-16T12:20:26.781610image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NACCAGENACCAGEBSEXEDUCMARISTATNACCLIVSRACEHISPANICHANDEDNACCFAMNACCMOMNACCDADTOBAC100SMOKYRSALCOHOLCVHATTCVAFIBCVCHFCBSTROKECBTIADIABETESHYPERTENHYPERCHONACCTBIAPNEADEP2YRSNACCBMIHEIGHTWEIGHTHEARINGHEARAIDBPSYSBPDIASNACCUDSD
070.070.0116.01.04.01.00.02.01.00.00.00.00.00.00.00.00.00.00.00.01.01.01.00.00.032.471.0232.01.00.0160.085.01
171.070.0116.01.02.01.00.02.01.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN30.771.0220.01.00.0175.084.01
266.066.0116.01.02.01.00.02.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.023.772.0175.01.00.0149.086.00
363.063.0216.01.02.01.01.02.0NaNNaNNaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN888.888.8NaN1.00.0NaNNaN1
477.077.0112.03.01.01.01.02.0NaN0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.019.065.0114.00.00.0148.075.00
581.081.027.03.01.01.01.02.01.00.00.01.027.00.01.00.00.00.00.00.01.01.0NaN0.00.022.163.0125.00.01.0139.046.01
686.086.0120.01.02.01.00.02.00.00.00.01.04.00.00.00.00.00.00.00.00.00.00.00.00.028.971.0207.00.01.0117.060.01
787.086.0120.01.02.01.00.02.00.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN27.871.0199.00.01.0152.070.01
874.074.0118.01.02.01.01.02.00.00.00.00.00.00.00.01.00.00.00.00.01.01.00.01.00.026.473.5203.00.01.0137.0NaN0
976.076.0114.01.02.01.00.01.01.01.00.00.00.00.00.00.00.00.00.00.01.01.00.00.00.030.471.0218.00.00.0135.0119.01
NACCAGENACCAGEBSEXEDUCMARISTATNACCLIVSRACEHISPANICHANDEDNACCFAMNACCMOMNACCDADTOBAC100SMOKYRSALCOHOLCVHATTCVAFIBCVCHFCBSTROKECBTIADIABETESHYPERTENHYPERCHONACCTBIAPNEADEP2YRSNACCBMIHEIGHTWEIGHTHEARINGHEARAIDBPSYSBPDIASNACCUDSD
19518653.053.0216.05.01.03.01.02.0NaN0.00.00.00.00.00.00.00.00.00.00.00.00.01.00.01.0888.888.8161.01.00.0116.071.00
19518754.053.0216.05.01.03.01.02.0NaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0
19518855.053.0216.05.01.03.01.02.0NaN0.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN26.264.9157.01.00.0118.076.01
19518970.070.0213.02.01.02.00.02.0NaN0.0NaN0.00.00.00.00.00.02.00.01.01.01.00.0NaN0.0888.888.8NaN0.01.0154.077.01
19519071.070.0213.04.01.02.00.02.0NaN0.0NaN0.00.00.00.00.00.01.00.01.01.01.00.0NaN1.0888.888.8NaN0.01.0120.060.01
19519172.070.0213.04.04.02.00.02.0NaN0.0NaN0.00.00.00.00.00.02.00.01.01.01.00.0NaN0.0888.888.8NaN0.01.0142.073.01
19519287.087.0113.02.01.01.00.02.0NaN0.00.01.040.00.01.00.00.02.00.00.01.01.00.0NaN0.026.871.7196.00.01.0157.060.01
19519389.087.0113.02.01.01.00.02.0NaN0.00.01.040.00.01.00.01.01.00.00.01.01.00.0NaNNaN27.172.0200.00.00.0132.071.01
19519489.087.0113.02.01.01.00.02.0NaN0.00.01.040.00.02.00.01.02.00.00.01.01.00.0NaN0.025.573.0193.00.01.0105.054.01
19519561.061.0218.04.03.01.00.01.01.01.01.00.00.00.00.00.00.00.00.00.00.00.01.00.00.026.759.0132.01.00.0104.055.00

Duplicate rows

Most frequently occurring

NACCAGENACCAGEBSEXEDUCMARISTATNACCLIVSRACEHISPANICHANDEDNACCFAMNACCMOMNACCDADTOBAC100SMOKYRSALCOHOLCVHATTCVAFIBCVCHFCBSTROKECBTIADIABETESHYPERTENHYPERCHONACCTBIAPNEADEP2YRSNACCBMIHEIGHTWEIGHTHEARINGHEARAIDBPSYSBPDIASNACCUDSD# duplicates
45872.070.0218.01.02.01.00.02.01.01.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN06
11765.063.0116.01.02.01.00.02.01.01.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN05
85779.075.0118.01.02.01.00.02.00.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN15
98281.077.0212.01.02.01.00.02.01.01.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN15
99281.078.0218.01.02.01.00.02.01.01.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN15
14966.064.0116.01.02.01.00.02.00.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14
19667.065.0216.01.02.01.00.02.00.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN04
21468.060.0216.01.02.01.00.02.01.01.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN04
28569.065.0218.01.02.01.00.02.00.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14
38771.069.0116.01.02.01.00.02.00.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN14